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Germany · Medical Device Components · PE-Backed Mid-Cap
Building an AI Growth Engine
PE-Backed Medical Device Components Manufacturer
12 wks
From kick-off to a managed new-customer engine, operator-led, no consultants on site
250+
Named decision makers mapped and captured into CRM
15 / 16
Platform makers across Western and Central Europe engaged
4-layer
Lightweight tech stack: CRM, AI contact intelligence, network signal, sync bridge
Situation
A German precision parts manufacturer with a strong technical reputation in drug-delivery device components aimed to capture share in the Medtech self-injection wave driven by GLP-1 weight-loss therapies. The commercial setup was the classic mid-cap industrial default: senior engineers and the commercial director carrying both delivery and hunting, targets identified through personal network and trade-fair contact, account intelligence sitting in slide decks and individual heads, no CRM beyond a spreadsheet. The opportunity wasn't lack of targets. It was lack of a system to work them. 25+ major autoinjector production sites across Western and Central Europe, run by ~15 identifiable platform makers, with hundreds of named decision makers across development, product management and procurement. None of it captured.
What worked
Twelve weeks, operator-led, no consultants on site. Rather than hire an account manager (a 12-month time-to-impact), build the system the account manager would need and run it with the existing technical sales team. The result was a four-layer tech stack: a lightweight CRM as system of record, an AI-driven contact intelligence platform for the company and decision-maker universe, a professional network sales tool for live signal and warm pathing, and a sync bridge that kept the three in alignment without manual data entry. No single layer is novel. Together they compress the commercial intelligence cycle from weeks to hours, at a cost that does not require board approval.
Impact
Early-stage visibility on account and competitor structures improved sharply, exposing the organisations behind decision makers and concentrating effort on the highest-probability, highest-value leads. Targeted outreach converted into multiple productive engagements and a new customer win with a multi-year sales tail. 250+ named contacts captured into the CRM, 15 of the 16 platform makers engaged, a weekly potential × probability steering view in place by week 12.
Design principles
The four principles that made it work
Every layer of the stack and every step of the sequence trace back to these. None of them are AI principles. They are the operating discipline that affordable AI tools finally make practical at this size of business.
Tools over headcount
A small monthly subscription stack beats a €100k+ account manager hire for the first 18 months, and is productive from week one.
One stack, one funnel, one cadence
Contact intelligence → network signal → CRM sync → system of record, end-to-end. No tool added unless it earned its place.
Depth profiles before mass outreach
A two-page account profile per platform maker before any contact. Cold calls land differently when the caller knows the customer's plant moves and partner ecosystem.
Honest status, weekly
The positioning grid is colour-coded by contact status. No "we're working on it". Either customer, in contact, no contact, or declined.
Read the complete case study
Full sequencing of the 12-week build, the four-layer tech stack, the depth-profile discipline, the weekly steering cadence, and the operating routine that affordable AI tools finally make practical at mid-cap scale.